Machine Methods
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| Author | : Frank Hutter |
| Publisher | : Springer |
| Total Pages | : 223 |
| Release | : 2019-05-17 |
| Genre | : Computers |
| ISBN | : 3030053180 |
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
| Author | : Guanghui Lan |
| Publisher | : Springer Nature |
| Total Pages | : 591 |
| Release | : 2020-05-15 |
| Genre | : Mathematics |
| ISBN | : 3030395685 |
This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
| Author | : Zhi-Hua Zhou |
| Publisher | : CRC Press |
| Total Pages | : 238 |
| Release | : 2012-06-06 |
| Genre | : Business & Economics |
| ISBN | : 1439830037 |
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.
| Author | : United States. Bureau of Labor |
| Publisher | : |
| Total Pages | : 1206 |
| Release | : 1899 |
| Genre | : Industries |
| ISBN | : |
| Author | : Alan Fielding |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 284 |
| Release | : 1999-08-31 |
| Genre | : Computers |
| ISBN | : 9780412841903 |
This is the first text aimed at introducing machine learning methods to a readership of professional ecologists. All but one of the chapters have been written by ecologists and biologists who highlight the application of a particular method to a particular class of problem.
| Author | : S. Y. Kung |
| Publisher | : Cambridge University Press |
| Total Pages | : 617 |
| Release | : 2014-04-17 |
| Genre | : Computers |
| ISBN | : 1139867636 |
Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.
| Author | : Management Association, Information Resources |
| Publisher | : IGI Global |
| Total Pages | : 1516 |
| Release | : 2022-05-13 |
| Genre | : Computers |
| ISBN | : 1668462923 |
Machine learning continues to have myriad applications across industries and fields. To ensure this technology is utilized appropriately and to its full potential, organizations must better understand exactly how and where it can be adapted. Further study on the applications of machine learning is required to discover its best practices, challenges, and strategies. The Research Anthology on Machine Learning Techniques, Methods, and Applications provides a thorough consideration of the innovative and emerging research within the area of machine learning. The book discusses how the technology has been used in the past as well as potential ways it can be used in the future to ensure industries continue to develop and grow. Covering a range of topics such as artificial intelligence, deep learning, cybersecurity, and robotics, this major reference work is ideal for computer scientists, managers, researchers, scholars, practitioners, academicians, instructors, and students.
| Author | : Dirk P. Kroese |
| Publisher | : CRC Press |
| Total Pages | : 538 |
| Release | : 2019-11-20 |
| Genre | : Business & Economics |
| ISBN | : 1000730778 |
Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
| Author | : Jordi Solé-Casals |
| Publisher | : MDPI |
| Total Pages | : 316 |
| Release | : 2021-08-17 |
| Genre | : Mathematics |
| ISBN | : 3036512888 |
Over the past years, businesses have had to tackle the issues caused by numerous forces from political, technological and societal environment. The changes in the global market and increasing uncertainty require us to focus on disruptive innovations and to investigate this phenomenon from different perspectives. The benefits of innovations are related to lower costs, improved efficiency, reduced risk, and better response to the customers’ needs due to new products, services or processes. On the other hand, new business models expose various risks, such as cyber risks, operational risks, regulatory risks, and others. Therefore, we believe that the entrepreneurial behavior and global mindset of decision-makers significantly contribute to the development of innovations, which benefit by closing the prevailing gap between developed and developing countries. Thus, this Special Issue contributes to closing the research gap in the literature by providing a platform for a scientific debate on innovation, internationalization and entrepreneurship, which would facilitate improving the resilience of businesses to future disruptions. Order Your Print Copy
| Author | : Radek Silhavy |
| Publisher | : Springer Nature |
| Total Pages | : 534 |
| Release | : |
| Genre | : |
| ISBN | : 3031705955 |
